{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "GEN_4_WGAN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM/uRSIpAIcXMBEh8qCN3NC",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/cxbxmxcx/GenReality/blob/master/GEN_4_WGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7A1WwPqxmfrE"
      },
      "source": [
        "IMPORTS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7Aa7VNjLmaw9"
      },
      "source": [
        "import math\n",
        "import os\n",
        "\n",
        "import torchvision.transforms as transforms\n",
        "from torchvision.utils import make_grid\n",
        "\n",
        "from torch.utils.data import DataLoader\n",
        "from torchvision import datasets\n",
        "from torch.autograd import Variable\n",
        "\n",
        "import torch.nn as nn\n",
        "import torch.nn.functional as F\n",
        "import torch\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "\n",
        "from tqdm import tqdm \n",
        "\n",
        "plt.ion()\n",
        "from IPython.display import clear_output"
      ],
      "execution_count": 163,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uH2Cn3nJmjKF"
      },
      "source": [
        "HYPERPARAMETERS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ACmQQa3emoYE",
        "outputId": "c48bafae-0070-40ae-be86-0fe9f4f996a2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "class Hyperparameters(object):\n",
        "  def __init__(self, **kwargs):\n",
        "    self.__dict__.update(kwargs)\n",
        "\n",
        "hp = Hyperparameters(n_epochs=200,\n",
        "                     batch_size=64,\n",
        "                     lr=0.00005,                     \n",
        "                     n_cpu=8,\n",
        "                     latent_dim=100,\n",
        "                     img_size=32,\n",
        "                     channels=1,\n",
        "                     n_critic=25,\n",
        "                     clip_value=.005,\n",
        "                     sample_interval=400)\n",
        "\n",
        "print(hp.lr)"
      ],
      "execution_count": 164,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5e-05\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQ954M71otK9"
      },
      "source": [
        "SETUP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dbDFpxOdonQI"
      },
      "source": [
        "os.makedirs(\"images\", exist_ok=True)\n",
        "img_shape = (hp.channels, hp.img_size, hp.img_size)\n",
        "\n",
        "cuda = True if torch.cuda.is_available() else False\n",
        "\n",
        "def weights_init_normal(m):\n",
        "  classname = m.__class__.__name__\n",
        "  if classname.find(\"Conv\") != -1:\n",
        "    torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n",
        "  elif classname.find(\"BatchNorm2d\") != -1:\n",
        "    torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n",
        "    torch.nn.init.constant_(m.bias.data, 0.0)\n",
        "\n",
        "def to_img(x):\n",
        "  x = x.clamp(0, 1)\n",
        "  return x\n",
        "\n",
        "def show_image(img):\n",
        "  img = to_img(img)\n",
        "  npimg = img.numpy()\n",
        "  plt.imshow(np.transpose(npimg, (1, 2, 0)))\n",
        "\n",
        "def visualise_output(images, x, y):\n",
        "  with torch.no_grad():  \n",
        "    images = images.cpu()\n",
        "    images = to_img(images)\n",
        "    np_imagegrid = make_grid(images, x, y).numpy()\n",
        "    figure(figsize=(20,20))\n",
        "    plt.imshow(np.transpose(np_imagegrid, (1, 2, 0)))\n",
        "    plt.show()"
      ],
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xIXAbyxIo9r9"
      },
      "source": [
        "GENERATOR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bTfDyWs-o_cG"
      },
      "source": [
        "class Generator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Generator, self).__init__()\n",
        "\n",
        "        def block(in_feat, out_feat, normalize=True):\n",
        "            layers = [nn.Linear(in_feat, out_feat)]\n",
        "            if normalize:\n",
        "                layers.append(nn.BatchNorm1d(out_feat, 0.8))\n",
        "            layers.append(nn.LeakyReLU(0.2, inplace=True))\n",
        "            return layers\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            *block(hp.latent_dim, 128, normalize=False),\n",
        "            *block(128, 256),\n",
        "            *block(256, 512),\n",
        "            *block(512, 1024),\n",
        "            nn.Linear(1024, int(np.prod(img_shape))),\n",
        "            nn.Tanh()\n",
        "        )\n",
        "\n",
        "    def forward(self, z):\n",
        "        img = self.model(z)\n",
        "        img = img.view(img.shape[0], *img_shape)\n",
        "        return img"
      ],
      "execution_count": 166,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P01xoE6FpL9-"
      },
      "source": [
        "DISCRIMINATOR"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jYlPm9G8pN6u"
      },
      "source": [
        "class Discriminator(nn.Module):\n",
        "    def __init__(self):\n",
        "        super(Discriminator, self).__init__()\n",
        "\n",
        "        self.model = nn.Sequential(\n",
        "            nn.Linear(int(np.prod(img_shape)), 512),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Linear(512, 256),\n",
        "            nn.LeakyReLU(0.2, inplace=True),\n",
        "            nn.Linear(256, 1),\n",
        "        )\n",
        "\n",
        "    def forward(self, img):\n",
        "        img_flat = img.view(img.shape[0], -1)\n",
        "        validity = self.model(img_flat)\n",
        "        return validity"
      ],
      "execution_count": 167,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0y-0F1URpfQd"
      },
      "source": [
        "LOSS and MODELS"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7UPqaoCSphQ1",
        "outputId": "338f0541-bf5e-4e28-ae9b-3557e0f5c198",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "generator = Generator()\n",
        "discriminator = Discriminator()\n",
        "\n",
        "if cuda:\n",
        "  generator.cuda()\n",
        "  discriminator.cuda()  \n",
        "\n",
        "# Initialize weights\n",
        "generator.apply(weights_init_normal)\n",
        "discriminator.apply(weights_init_normal)"
      ],
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Discriminator(\n",
              "  (model): Sequential(\n",
              "    (0): Linear(in_features=1024, out_features=512, bias=True)\n",
              "    (1): LeakyReLU(negative_slope=0.2, inplace=True)\n",
              "    (2): Linear(in_features=512, out_features=256, bias=True)\n",
              "    (3): LeakyReLU(negative_slope=0.2, inplace=True)\n",
              "    (4): Linear(in_features=256, out_features=1, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y0X-PJWYp6W9"
      },
      "source": [
        "OPTIMIZERS and TENSOR SETUP"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ymTI34yKqA2u"
      },
      "source": [
        "optimizer_G = torch.optim.RMSprop(generator.parameters(), lr=hp.lr)\n",
        "optimizer_D = torch.optim.RMSprop(discriminator.parameters(), lr=hp.lr)\n",
        "\n",
        "Tensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor"
      ],
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DJzNJIrGqJyG"
      },
      "source": [
        "GET the DATA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oeYA8ZRCqMYl"
      },
      "source": [
        "os.makedirs(\"../../data/mnist\", exist_ok=True)\n",
        "dataloader = torch.utils.data.DataLoader(\n",
        "  datasets.FashionMNIST(\n",
        "    \"../../data/mnist\",\n",
        "    train=True,\n",
        "    download=True,\n",
        "    transform=transforms.Compose(\n",
        "        [transforms.Resize(hp.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]\n",
        "    ),\n",
        "  ),\n",
        "  batch_size=hp.batch_size,\n",
        "  shuffle=True,\n",
        ")"
      ],
      "execution_count": 170,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bf3lQ1-0qehW"
      },
      "source": [
        "TRAINING"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ipLMbwcxqgPm",
        "outputId": "ce163725-d02b-42d0-b974-b2f86e9f868d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "for epoch in range(hp.n_epochs):\n",
        "  for i, (imgs, _) in enumerate(dataloader):\n",
        "\n",
        "      # Adversarial ground truths\n",
        "      valid = Variable(Tensor(imgs.shape[0], 1).fill_(1.0), requires_grad=False)\n",
        "      fake = Variable(Tensor(imgs.shape[0], 1).fill_(0.0), requires_grad=False)\n",
        "\n",
        "      # Configure input\n",
        "      real_imgs = Variable(imgs.type(Tensor))\n",
        "\n",
        "      # -----------------\n",
        "      #  Train Discriminator\n",
        "      # -----------------\n",
        "\n",
        "      optimizer_G.zero_grad()\n",
        "\n",
        "      # Sample noise as generator input\n",
        "      z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], hp.latent_dim))))\n",
        "\n",
        "      # Generate a batch of images\n",
        "      fake_imgs = generator(z).detach()\n",
        "\n",
        "      d_loss = -torch.mean(discriminator(real_imgs)) + torch.mean(discriminator(fake_imgs)) \n",
        "\n",
        "      d_loss.backward()\n",
        "      optimizer_D.step()\n",
        "\n",
        "      # Clip weights of discriminator\n",
        "      for p in discriminator.parameters():\n",
        "        p.data.clamp_(-hp.clip_value, hp.clip_value)\n",
        "\n",
        "\n",
        "      # Train the generator every n_critic iterations\n",
        "      if i % hp.n_critic == 0:\n",
        "        # ---------------------\n",
        "        #  Train Generator\n",
        "        # ---------------------\n",
        "        optimizer_G.zero_grad()\n",
        "\n",
        "        # Generate a batch of images\n",
        "        gen_imgs = generator(z)\n",
        "        # Adversarial loss\n",
        "        g_loss = -torch.mean(discriminator(gen_imgs))\n",
        "\n",
        "        g_loss.backward()\n",
        "        optimizer_G.step()    \n",
        "\n",
        "      batches_done = epoch * len(dataloader) + i\n",
        "      if batches_done % hp.sample_interval == 0:\n",
        "        clear_output()\n",
        "        print(f\"Epoch:{epoch}:It{i}:DLoss{d_loss.item()}:GLoss{g_loss.item()}\")          \n",
        "        visualise_output(gen_imgs.data[:50],10, 10)\n",
        "          #save_image(gen_imgs.data[:25], \"images/%d.png\" % batches_done, nrow=5, normalize=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch:0:It400:DLoss-2.5848448276519775:GLoss-0.6472996473312378\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}